ZK-Value is a practical ZK system for verifiable Shapley-value data valuation using LSH approximations and optimized proofs that matches baseline quality while generating proofs in seconds to minutes.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Succinct Model Difference Proofs certify that a neural-network update stays inside a policy-defined drift class using zero-knowledge proofs whose cost depends only on the drift structure.
citing papers explorer
-
ZK-Value: A Practical Zero-Knowledge System for Verifiable Data Valuation
ZK-Value is a practical ZK system for verifiable Shapley-value data valuation using LSH approximations and optimized proofs that matches baseline quality while generating proofs in seconds to minutes.
-
Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates
Succinct Model Difference Proofs certify that a neural-network update stays inside a policy-defined drift class using zero-knowledge proofs whose cost depends only on the drift structure.